End-to-end Material Thermal Conductivity Prediction through Machine Learning
This work addresses the problem of accelerating material screening for researchers in materials science, but it is incremental as it builds on existing methods with limited accuracy improvements.
The study tackled predicting material thermal conductivity using machine learning by expanding the dataset through high-throughput calculations and introducing a graph-based neural network to reduce overfitting, achieving a best mean absolute percentage error of 50-60% on test data.
We investigated the accelerated prediction of the thermal conductivity of materials through end- to-end structure-based approaches employing machine learning methods. Due to the non-availability of high-quality thermal conductivity data, we first performed high-throughput calculations based on first principles and the Boltzmann transport equation for 225 materials, effectively more than doubling the size of the existing dataset. We assessed the performance of state-of-the-art machine learning models for thermal conductivity prediction on this expanded dataset and observed that all these models suffered from overfitting. To address this issue, we introduced a novel graph-based neural network model, which demonstrated more consistent and regularized performance across all evaluated datasets. Nevertheless, the best mean absolute percentage error achieved on the test dataset remained in the range of 50-60%. This suggests that while these models are valuable for expediting material screening, their current accuracy is still limited.